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dc.contributor.advisorBuckland, S. T. (Stephen T.)
dc.contributor.advisorBorchers, D. L.
dc.contributor.advisorKühl, H. (Hjalmar)
dc.contributor.authorHowe, Eric J.
dc.coverage.spatialxv, 236 p.en_US
dc.description.abstractAll species and subspecies of African great apes are listed by the International Union for the Conservation of Nature as endangered or critically endangered, and populations continue to decline. As human populations and industry expand into great ape habitat, efficient, reliable estimators of great ape abundance are needed to inform conservation status and land-use planning, to assess adverse and beneficial effects of human activities, and to help funding agencies and donors make informed and efficient contributions. Fortunately, technological advances have improved our ability to sample great apes remotely, and new statistical methods for estimating abundance are constantly in development. Following a brief general introduction, this thesis reviews established and emerging approaches to estimating great ape abundance, then describes new methods for estimating animal density from photographic data by distance sampling with camera traps, and for selecting among models of the distance sampling detection function when distance data are overdispersed. Subsequent chapters quantify the effect of violating the assumption of demographic closure when estimating abundance using spatially explicit capture–recapture models for closed populations, and describe the design and implementation of a camera trapping survey of chimpanzees at the landscape scale in Kibale National Park, Uganda. The new methods developed have generated considerable interest, and allow abundances of multiple species, including great apes, to be estimated from data collected during a single photographic survey. Spatially explicit capture–recapture analyses of photographic data from small study areas yielded accurate and precise estimates of chimpanzee abundance, and this combination of methods could be used to enumerate great apes over large areas and in dense forests more reliably and efficiently than previously possible.en_US
dc.description.sponsorship"This work was supported by a St Leonard’s College Scholarship from the University of St Andrews, and the Max Planck Institute for Evolutionary Anthropology." -- Fundingen
dc.publisherUniversity of St Andrews
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectCamera trappingen_US
dc.subjectDistance samplingen_US
dc.subjectSpatially explicit capture-recaptureen_US
dc.subjectModel selectionen_US
dc.subjectPan troglodytesen_US
dc.subject.lcshMammal populations--Estimatesen
dc.subject.lcshSampling (Statistics)en
dc.titleEstimating abundance of African great apesen_US
dc.contributor.sponsorMax-Planck-Institut für Evolutionäre Anthropologieen_US
dc.contributor.sponsorUniversity of St Andrews. St Leonard's College Scholarshipen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US
dc.publisher.institutionThe University of St Andrewsen_US
dc.publisher.departmentCentre for Research into Ecological and Environmental Modellingen_US

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    Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
    Except where otherwise noted within the work, this item's licence for re-use is described as Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International